{"id":"W3165762224","doi":"","title":"INVESTIGATING THE PARAMETERS OF PRE-/POST-CONDITIONING ON HUMAN-DERIVED CANCER CELLS","year":2019,"lang":"en","type":"dissertation","venue":"MacSphere (McMaster University)","topic":"Chemical Reactions and Isotopes","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Bruce Power; McMaster University","keywords":"Conditioning; Cancer; Biology; Cancer research; Computational biology; Medicine; Mathematics; Internal medicine; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002883207,0.0002485937,0.0003549075,0.0002041776,0.0001628986,0.0003967754,0.0002002211,0.0003116216,0.004500187],"category_scores_gemma":[0.0004408565,0.0001003132,0.000311255,0.0003249292,0.0001941794,0.0002947193,0.0001894562,0.0007327097,0.0008393379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003115893,"about_ca_system_score_gemma":0.0002071005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007710725,"about_ca_topic_score_gemma":0.001000391,"domain_scores_codex":[0.9996811,0.00005101578,0.00002687048,0.00007199374,0.0001151962,0.00005386994],"domain_scores_gemma":[0.9997346,0.00008549917,0.00004494799,0.0000405981,0.00006898157,0.00002534802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003557344,0.0001231563,0.001568831,0.0002325263,0.00001806795,0.0000992756,0.0001426425,0.0002931712,0.9889386,0.0001795421,0.0002316653,0.00781686],"study_design_scores_gemma":[0.000009451292,0.00158802,0.02204894,0.00003332437,0.00005048462,0.0002838719,0.0001939639,0.0005485378,0.9677627,0.0001122866,0.007352877,0.00001546673],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796236,0.007689537,0.005193137,0.0002025623,0.0001216859,0.0001377291,0.001339359,0.0001059277,0.005586562],"genre_scores_gemma":[0.9880612,0.003094136,0.002498112,0.0002253257,0.00002553748,0.0001447447,0.001637215,0.00004635933,0.004267223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004500187,"threshold_uncertainty_score":0.01505458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07108236300176524,"score_gpt":0.3580790723583085,"score_spread":0.2869967093565433,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}